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- abstractApril 2020
CNN 101: Interactive Visual Learning for Convolutional Neural Networks
- Zijie J. Wang,
- Robert Turko,
- Omar Shaikh,
- Haekyu Park,
- Nilaksh Das,
- Fred Hohman,
- Minsuk Kahng,
- Duen Horng Chau
CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing SystemsPages 1–7https://doi.org/10.1145/3334480.3382899The success of deep learning solving previously-thought hard problems has inspired many non-experts to learn and understand this exciting technology. However, it is often challenging for learners to take the first steps due to the complexity of deep ...
- research-articleJanuary 2019
GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation
IEEE Transactions on Visualization and Computer Graphics (ITVC), Volume 25, Issue 1Pages 310–320https://doi.org/10.1109/TVCG.2018.2864500Recent success in deep learning has generated immense interest among practitioners and students, inspiring many to learn about this new technology. While visual and interactive approaches have been successfully developed to help people more easily learn ...
- research-articleJune 2016
Visual exploration of machine learning results using data cube analysis
HILDA '16: Proceedings of the Workshop on Human-In-the-Loop Data AnalyticsArticle No.: 1, Pages 1–6https://doi.org/10.1145/2939502.2939503As complex machine learning systems become more widely adopted, it becomes increasingly challenging for users to understand models or interpret the results generated from the models. We present our ongoing work on developing interactive and visual ...